Graph neural networks have emerged as a leading paradigm for inferring node labels in complex relational data. By extending convolutional and attention operations to arbitrary graph structures, these ...
Abstract: Recent years have witnessed fast developments of graph neural networks (GNNs) that have benefited myriad graph analytic tasks and applications. Most GNNs rely on the homophily assumption ...
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